Neural substrate of body size: illusory feeling of shrinking of the waist.

Abstract

The perception of the size and shape of one's body (body image) is a fundamental aspect of how we experience ourselves. We studied the neural correlates underlying perceived changes in the relative size of body parts by using a perceptual illusion in which participants felt that their waist was shrinking. We scanned the brains of the participants using functional magnetic resonance imaging. We found that activity in the cortices lining the left postcentral sulcus and the anterior part of the intraparietal sulcus reflected the illusion of waist shrinking, and that this activity was correlated with the reported degree of shrinking. These results suggest that the perceived changes in the size and shape of body parts are mediated by hierarchically higher-order somatosensory areas in the parietal cortex. Based on this finding we suggest that relative size of body parts is computed by the integration of more elementary somatic signals from different body segments.

The Position of the Two Hands Relative to the Body and the Factorial Design of the Experiment

When the palms of the hands were in contact with the body, the vibration of the two wrists elicited the illusion that the wrists were passively flexing and the waist and hips were shrinking (A, lower right). When the hands were not in contact with the body, the vibration of the wrists only elicited the illusion that the hands were flexing (A, top right). In two additional conditions, we vibrated the skin over the styloid bone beside the tendon, which does not elicit any illusions (A, top left and lower left). The neural effect of the shrinking-body illusion can be modelled as the interaction term between hand position and site of vibration in a 2 × 2 factorial design (see [B], [tendon contact– skin contact] – [tendon free – skin free]).

Activity that Reflects the Illusion that the Waist Was Shrinking (Interaction Effect: p < 0.001 Uncorrected)

Top row: Activation of the cortices lining postcentral sulcus near its junction with the intraparietal sulcus. Lower row: Activation of cortex lining the intraparietal sulcus. The activations (colour) are superimposed on a normalized high-resolution T1-weighted image of a representative participant (black and white). The coordinates for the displayed slices are shown, and the crossing of the blue lines indicates the location of the activation peaks. R and L denote the right and left hemispheres, respectively. The plots to the right show the contrast estimates with the standard bars corresponding to the standard error (SE).

Linear Relationship between Parietal Activity and the Strength of the Shrinking-Waist Illusion

Each dot represents the values for one individual subject. The data is fitted with a least-squares regression line. In (A) and (B) we plot the activity from the peaks in the intraparietal region that showed the most significant relation between illusion strength and neuronal activity ([A]: x = −39, y = −42, z = 72, t = 3.81; p < 0.001 uncorrected, R2 = 0.4916, Pearson's R = 0.70; [B]: x = −48, y = −33, z = 54; t = 2.69; p < 0.009 uncorrected, R2 = 0.32, Pearson's R = 0.57). These peaks were identified by using SPM2 to search for parietal voxels using a second-level linear regression model. (C) and (D) show the relationship between illusion and activity at exactly those peak voxels detected in the interaction analysis (see ). (C) shows the cortex at the junction between the postcentral sulcus and the intraparietal sulcus (p < 0.027, y = 0.0385x + 0.1424, R2 = 0.23, Pearson's R = 0.48), and (D) illustrates the anterior part of the intraparietal cortex (p < 0.016, y = 0.0552x + 0.0488, R2 = 0.27, Pearson's R = 0.52). In all plots the y-axis indicates the BOLD response (contrast estimates for interaction effect) in the parietal cortex, and the x-axis indicates the illusory displacement of the wrists when in contact with the body (which corresponds to the degree of waist shrinking). These regressions are not driven by outliers because all four remained significant (p < 0.05) when we used a least-square fitting procedure that minimizes the effects of outliers (Robustfit in MATLAB; for details).